CompARE: A Computational framework for Airborne Respiratory disease Evaluation integrating flow physics and human behavior
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908677563219968 |
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| author | Leong, Fong Yew Kwak, Jaeyoung Ge, Zhengwei Ooi, Chin Chun Fong, Siew-Wai Tay, Matthew Zirui Qian, Hua Kang, Chang Wei Cai, Wentong Li, Hongying |
| author_facet | Leong, Fong Yew Kwak, Jaeyoung Ge, Zhengwei Ooi, Chin Chun Fong, Siew-Wai Tay, Matthew Zirui Qian, Hua Kang, Chang Wei Cai, Wentong Li, Hongying |
| contents | The risk of indoor airborne transmission among co-located individuals is generally non-uniform, which remains a critical challenge for public health modelling. Thus, we present CompARE, an integrated risk assessment framework for indoor airborne disease transmission that reveals a striking bimodal distribution of infection risk driven by airflow dynamics and human behavior. Combining computational fluid dynamics (CFD), machine learning (ML), and agent-based modeling (ABM), our model captures the complex interplay between aerosol transport, human mobility, and environmental context. Based on a prototypical childcare center, our approach quantifies how incorporation of ABM can unveil significantly different infection risk profiles across agents, with more than two-fold change in risk of infection between the individuals with the lowest and highest risks in more than 90% of cases, despite all individuals being in the same overall environment. We found that infection risk distributions can exhibit not only a striking bimodal pattern in certain activities but also exponential decay and fat-tailed behavior in others. Specifically, we identify low-risk modes arising from source containment, as well as high-risk tails from prolonged close contact. Our approach enables near-real-time scenario analysis and provides policy-relevant quantitative insights into how ventilation design, spatial layout, and social distancing policies can mitigate transmission risk. These findings challenge simple distance-based heuristics and support the design of targeted, evidence-based interventions in high-occupancy indoor settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21782 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CompARE: A Computational framework for Airborne Respiratory disease Evaluation integrating flow physics and human behavior Leong, Fong Yew Kwak, Jaeyoung Ge, Zhengwei Ooi, Chin Chun Fong, Siew-Wai Tay, Matthew Zirui Qian, Hua Kang, Chang Wei Cai, Wentong Li, Hongying Physics and Society Computers and Society Multiagent Systems The risk of indoor airborne transmission among co-located individuals is generally non-uniform, which remains a critical challenge for public health modelling. Thus, we present CompARE, an integrated risk assessment framework for indoor airborne disease transmission that reveals a striking bimodal distribution of infection risk driven by airflow dynamics and human behavior. Combining computational fluid dynamics (CFD), machine learning (ML), and agent-based modeling (ABM), our model captures the complex interplay between aerosol transport, human mobility, and environmental context. Based on a prototypical childcare center, our approach quantifies how incorporation of ABM can unveil significantly different infection risk profiles across agents, with more than two-fold change in risk of infection between the individuals with the lowest and highest risks in more than 90% of cases, despite all individuals being in the same overall environment. We found that infection risk distributions can exhibit not only a striking bimodal pattern in certain activities but also exponential decay and fat-tailed behavior in others. Specifically, we identify low-risk modes arising from source containment, as well as high-risk tails from prolonged close contact. Our approach enables near-real-time scenario analysis and provides policy-relevant quantitative insights into how ventilation design, spatial layout, and social distancing policies can mitigate transmission risk. These findings challenge simple distance-based heuristics and support the design of targeted, evidence-based interventions in high-occupancy indoor settings. |
| title | CompARE: A Computational framework for Airborne Respiratory disease Evaluation integrating flow physics and human behavior |
| topic | Physics and Society Computers and Society Multiagent Systems |
| url | https://arxiv.org/abs/2511.21782 |